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Found 11 papers, 2 papers with code
Declarative Recursive Computation on an RDBMS, or, Why You Should Use a Database For Distributed Machine Learning
A number of popular systems, most notably Google's TensorFlow, have been implemented from the ground up to support machine learning tasks.
Tensor Relational Algebra for Machine Learning System Design
This implementation abstraction provides little built-in support for ML systems to scale past a single machine, or for handling large models with matrices or tensors that do not easily fit into the RAM of an ASIC.
It's the Best Only When It Fits You Most: Finding Related Models for Serving Based on Dynamic Locality Sensitive Hashing
However, preparation of training data is often a bottleneck in the lifecycle of deploying a deep learning model for production or research.
Survive the Schema Changes: Integration of Unmanaged Data Using Deep Learning
Data is the king in the age of AI.
Benchmark of DNN Model Search at Deployment Time
Deep learning has become the most popular direction in machine learning and artificial intelligence.
A Comparison of Decision Forest Inference Platforms from A Database Perspective
In addition, the relation-centric representation we proposed significantly improved netsDB's performance in handling large-scale models, while the model reuse optimization we proposed further improved netsDB's performance in handling small-scale datasets.
Serving Deep Learning Model in Relational Databases
Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains, sparking growing interest recently.
FormalGeo: An Extensible Formalized Framework for Olympiad Geometric Problem Solving
In this paper, we have constructed a consistent formal plane geometry system.
A Learning-based Declarative Privacy-Preserving Framework for Federated Data Management
It is challenging to balance the privacy and accuracy for federated query processing over multiple private data silos.
FGeo-DRL: Deductive Reasoning for Geometric Problems through Deep Reinforcement Learning
We built a neural-symbolic system, called FGeoDRL, to automatically perform human-like geometric deductive reasoning.
FGeo-TP: A Language Model-Enhanced Solver for Geometry Problems
The application of contemporary artificial intelligence techniques to address geometric problems and automated deductive proof has always been a grand challenge to the interdiscipline field of mathematics and artificial Intelligence.
